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Dr. Ekanath Rangan received his MBBS from Amrita School of Medicine, a top-5 ranked University in India, winning Gold Medals for highest scores in general medicine and surgery. He has remarkable level of initiative and innovation in the synergistic intersection of medicine, wearable sensors, and artificial intelligence. He has co-authored numerous papers in reputed international journals and conferences and holds two US patents which propose novel systems for IoT based remote monitoring, smart and connected m-health, and techniques for data to decisions so as to deliver the 3P's of modern medicine: precision, personalization, and prevention. Particularly noteworthy, are his deep learning LSTM techniques for non-invasive single sensor based sleep apnea diagnosis.In addition to architecting a COVID remote patient monitoring system for risk stratification and severity prediction, he is also a co-PI on Indian Government funded Indo-US project for discovery of early warning biomarkers of COVID-19.Ekanath is a recipient of US NSF fellowship (2015) and excellence award for a talk titled "Rapid Health Alerts Using Multiple Sensors” delivered at University of California-San Francisco Bioengineering symposium (2016). At Amrita, he organized the first of its kind Research Synergy Meet, bringing together more than 50 researchers in medicine, engineering, and computer science from five different campuses, to deliberate on clinical problems and digital solutions.
My research interests span: Wearable medical systems for non-invasive and pervasive health monitoring in cardiovascular and critical care contexts; Correlation of genomic aspects of disease with phenotypic data from electronic health records; Informatics and machine learning for precise detection and early warning of infectious diseases; Preemptive protocols for managing disease severity trajectories; And IoT based Telemedicine. My current work in Snyder Lab involves large scale study of resting heart rate, clinical symptoms and daily activities as they relate to COVID-19, as well as their time series big data analysis for risk stratification.